Automated TEM/SEM Image Analysis for Drug-Loaded Nanoparticles Using Machine Learning
Code: G-1851
Authors: Esmaiel Zeynalazar Sharabiani ℗, Ali Yousefi *
Schedule: Not Scheduled!
Tag: Drug Discovery
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Abstract:
Abstract
Background and aims: The precise characterization of drug-loaded nanoparticles is essential for evaluating their efficacy in drug delivery applications. Transmission Electron Microscopy (TEM) and Scanning Electron Microscopy (SEM) are standard techniques for nanoparticle imaging, yet manual analysis remains labor-intensive and subject to variability. This study proposes a machine learning-driven approach to automate TEM/SEM image analysis, enabling accurate and efficient identification of drug-loaded nanoparticles. Method: A comprehensive dataset was curated from two primary sources: (1) publicly available repositories such as NanoMine and Material Data Facility (MDF), which provide high-resolution TEM/SEM images of nanoparticles, and (2) peer-reviewed scientific literature featuring relevant microscopy images of drug-loaded nanoparticles. Image preprocessing techniques, including noise reduction, contrast enhancement, and feature extraction, were employed to optimize input quality. A Convolutional Neural Network (CNN) model was developed alongside conventional machine learning classifiers such as Support Vector Machine (SVM) and Random Forest to categorize nanoparticles based on morphology, drug-loading efficiency, and surface characteristics. Performance was evaluated using key metrics including accuracy, precision, recall, and F1-score. Results: The CNN-based model exhibited superior classification performance, achieving an accuracy of 94.2%. It effectively distinguished between loaded and unloaded nanoparticles with high specificity and sensitivity, demonstrating its potential as a reliable alternative to manual assessment. Conclusion: Leveraging machine learning for TEM/SEM image analysis significantly enhances the efficiency and consistency of nanoparticle characterization in drug delivery research. By reducing reliance on manual interpretation, this approach minimizes human bias and accelerates analytical workflows. Future efforts will focus on expanding the dataset and incorporating advanced AI methodologies, such as transfer learning, to further refine model accuracy and generalizability
Keywords
Artificial Intelligence, Nanoparticles, Machine Learning, TEM/SEM
